L4 Data Analysis Apprenticeship with Cambridge Spark
Course Content
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- Python (Cambridge Spark focuses on the use of Python)1
- Data privacy, ethics and regulations
- Data visualisations (mainly done in Python)
- Power BI & Tableau (optional)
- Databases & SQL2
Course Content (continued)
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- Why data science is good for businesses
- Maths for data science
- Time series analysis
- Descriptive, Predictive and Prescriptive analytics
- Introduction to machine learning
- Predictive analysis e.g. forecasting and categorization
- Text-mining, JSON and APIs
Knowledge, Skills and Behaviours (KSBs)
- Two separate sets: one set has to be demonstrated across your portfolio, one has to be demonstrated during your EPA project
- Knowledge: current legislation, data types, principles of statistics & different types of analytics
- Skills: apply statistical methodologies, convert data into visualisations, assess the impact of user experience
- Behaviours: demonstrate initiative and resilience, work independently and collaboratively
The Data Analysis Life Cycle
Timeline & Commitment
- Approximately 16 months from the introductory webinar to the End Point Assessment
- Typically 3 projects that form a portfolio of work + 1 End Point Assessment project
- “Off The Job” hours: minimum 6 hours per week.
- The End Point Assessment
- A professional discussion focusing on your portfolio
- A presentation on your End Point Assessment project
- Questions about your End Point Assessment project
Projects that I undertook
- Automation of data processing for the Non-Bedded Community Modelling Tool
- Improving visualisations for the HIOW Mental Health S136 report
- Exploration of forecasting methods applied to cancer waiting times data
- Using regression to predict improvement in Mental Health PROMs scores
- Investigating the relationship between health inequalities factors on Adult Social Care waiting times, and those waiting times on Emergency Department attendances
What I have done since
- Contributed to the running of Code Club
- Developed a Python script that searches Outlook and downloads attachments
- Supported testing Fabric as a platform for hosting data science projects by creating test machine learning workflows
- Wrote Python webscraping and data concatenation scripts to speed up data processing on a number of projects.
- Overlaid Fingertips data onto maps to support preliminary investigations for a Transformation & Consultancy project.
Reflections
- Keep the Data Analysis Life Cycle in mind for all your projects
- Have the list of KSBs open when you are scoping and writing up your projects
- Stay focused on the STAR method when writing up your portfolio projects
- Situation
- Task
- Action
- Result